The Oscars' Commercial Impact

The Oscars’ Commercial Impact: Some Evidence from

International Data

Fernanda Gutierrez-Navratil Universidad Pública de Navarra

PRELIMINARY DRAFT PLEASE DO NOT QUOTE Abstract: This paper aims to measure the value, in terms of their impact on weekly box-office revenues, of the most important Oscar nominations and awards (best picture, best director, best actor/actress, best supporting actor/actress and best original and adapted screenplay). This impact has seldom been measured controlling for the unobserved heterogeneity of movies, which is crucial since awards and nominations are usually allocated to high quality films. Also, we distinguish between the effect of a nomination before and after the Oscars are awarded. Thus, we test whether the effects change after the Oscar ceremony, since the audience could pay more attention to nominated films after that. Besides, we check whether it really matters which film win the Oscar. Then, we focused on the most important category, the best film award. We study the decision taken by the Academy in 2009 to raise the number of nominations. We test whether this decision has affected the commercial impact of nominations, because some members of the Academy have been campaigning for a return to five best film nominations. Finally, we analysed whether the commercial impact of an Oscar or a nomination in this category differs between the domestic and foreign markets, due to the particular characteristics of each market. We use weekly data on revenues from the USA and the main European markets and exploit the differences in the release schedules of these countries to disentangle the impact of awards and quality on movies demand. The panel data structure with three dimensions: movie, week and country, allow us to control not only for the perceived “quality” of each movie in each country, but also for the temporal decay pattern of the weekly box office revenues by film and by country. Key words: Panel data, Movies Performance, Box-office revenues, Oscar nominations and awards

1. Introduction

Producers and distributors try to take as much commercial advantage as possible from the “free” promotion implied by Oscar nominations and awards. Those films that have a chance to be nominated are intentionally released in the United States at the end of the year in order to be eligible and also to be on screen when nominations are announced and Oscars are awarded. However, when a film is no longer on screen and is nominated, it is very likely that it will be re-released in movie theatres to capitalize on the nomination.

Clearly, the expected impact of a nomination or award will depend on the Oscar category (Best Picture, Best Director, Best Actor/Actress, Best Supporting Actor/Actress, and Best Screenplay). The Best Picture award is expected to have the largest effect but it is also important to know the magnitude and the relative importance of all the other categories. Additionally, the expected impact of nominations and awards could differ between the domestic market and foreign markets (due to specific characteristics of each country). This impact could also change when there is a redesign of awards rules, for instance, in 2009 when the Academy decided to raise the number of nominations in the best picture category.

With this information in mind, producers would be able to take better decisions regarding budgets (allocation of resources), especially for films that are expected to compete in these nominations and awards. Distributors could decide about the release schedules of these films in domestic and foreign markets. For the Academy, this information could also be useful for an eventual redesign of the awards rules to generate higher value for the industry.

Accordingly, this paper attempts to measure the value of the most important Oscar nominations and awards in terms of their impact on box office revenues. This impact has seldom been measured using panel data techniques that allow controlling for differences in movies’ marketability and quality via the estimation of individual effects, which are inherent to this market. Controlling for individual effects is crucial since nominations and awards are not randomly allocated and usually go to higher quality movies. 1

Starting with Litman (1983), this topic has been the subject of a host of papers using regression models to determine what is important in explaining box office results. Most authors find that nominations and awards (which are separated by a couple of weeks) have a significant effect on revenues (see, for instance, Hirschman and Pieros 1985; Dodds and

Holbrook 1988; Ravid 1999; Simonoff and Sparrow 2000). Most of these papers are, however, unable to separate the effect of the quality of a movie from the award effect since they use box office results in one country only, 2,3 which makes it difficult to control for the heterogeneity of movies.

1 See however Ginsburgh and Weyers (2014) who, focusing on the period 1929–1995, show that many nominated are of better quality than those that received the Oscar for Best Movie. Authors looked at fifteen

100 Top Movie Lists and use as a quality indicator the number of lists in which a movie appears.

2 With exception of Craig et al. (2005), who consider several countries but are not interested in the effect of awards. There are also examples of papers that do not take advantage of the panel structure of their data set.

For instance, Elberse and Eliashberg (2003) “opt for a model that does not capture unobserved individual specific effects” and they run the regression separately for each country in their sample.

3 United States: Chen et al. (2013), Deuchert et al. (2005), Ginsburgh (2003), Nelson et al. (2001), East Asia:

Lee (2009).

We use a sample that consists of the weekly box office revenues (starting with the week of release, and including the following 40 weeks if the run is long enough) of the 150 top box office films released each year in each country (the United States, the United

Kingdom, France, Germany and Spain) from 2002 to 2015. We use the three dimensions of this panel structure (movie, week and country) to control not only for the perceived

“quality” of each movie in each country, but also for the temporal decay pattern of the weekly box office revenues by film and by country.

This makes it possible to separate the effect of the quality of a movie from the effect of awards on demand. Also, we are able to measure the effect of awards from the week in which a movie was nominated or awarded and to distinguish between the impact of nominations before and after the Oscar ceremony. Since the audience could pay more attention to nominated films after the Oscars ceremony, given its popularity. Besides, it also allows us to check whether it really matters which film win the Oscar.

We also exploit the differences in the release dates of movies in these countries to better evaluate the impact of nominations and awards. This framework permits a comparison of revenues before and after the announcement of nominations and awards and an assessment of the impact at particular points in time by matching running weeks in different countries, some before and others after the nominations. The impact of nominations on box office revenues may also change over time. For instance, if the nomination is revealed at an early stage of the commercial run of the movie, one can expect the (weekly) impact to be greater. To take this into account, we estimate a weekly box- office revenue model specified in semi-log form.

In the next section we describe the sample and database used. Section 3 discusses the empirical specifications and the estimation method. We present the principal results in 4

Section 4. In section 5 and 6, we check the robustness of the results and present additional results respectively. Finally, the conclusions and management implications are presented in

Section 7.

2. Sample and database

The sample we use for our empirical analysis comprises the 150 top box-office films released each year in each country (the United States, the United Kingdom, France,

Germany and Spain) from January 1st, 2002 to December 31st, 2015. We have collected the information provided by A. C. Nielsen EDI on movies released in the United States and the four largest European motion pictures markets (United Kingdom, France, Germany and

Spain). For each movie and country, our dataset includes information on titles, weekly and cumulative box-office revenue, number of theatres on each week of exhibition, distributors, official release dates and certain film characteristics (identification of sequels, MPAA rating, etc.). In the case of France we have weekly and cumulative attendance instead of box-office revenues. We use the information provided by the Box Office Mojo website

(boxofficemojo.com) relating to the movies nominated and awarded with Oscars in the different categories.

We also use information about the different movie titles in each country provided by the Internet Movie Database web site (imdb.com), to match movies across countries. Thus, we obtain a panel data structure with three dimensions: movie, week and country. We use the three dimensions of this dataset to better assess the impact of Oscar nominations and awards on box-office revenues.

We have removed from the sample, those movie-week combinations observed only in one country, in order to achieve a better identification of temporal patterns. This implies that movies released in only one country were removed from the sample. Also, we withdrew all movies with short runs (one month or shorter runs), as we are interested in the impact of nominations and awards and there is a 5-6 week gap between both announcements. Thus, the final sample comprises 2,063 movies: 1,212 released in France,

1,557 in Germany, 1,640 in Spain, 957 in the UK and 1,622 in the USA. Although movies must be released in the US (Los Angeles County) in the previous year to qualify for the

Academy Awards (except for the Best Foreign Language Film), our sample include some films not released in the US as part of the reference category. These movies are also high box office revenues films that permit the comparison with films that run for the Oscars providing useful information to estimate the effects of Oscars by country and time.

3. The Empirical Model

In accordance with the previous literature, we estimate a weekly box-office revenue equation that includes as independent variables the characteristics that determine the quality and the marketability of the film, the availability of the movie and the time and seasonality in the underlying demand. Considering all these determinants, we define the following empirical model:

_ = + + + + + + + _ℎ

+ _() + _()

+ () + (1)

6 where subscript i denotes the movie, t represents the exhibition week in which the film revenues are collected, subscript c stands for the country where it was exhibited and εict is the error term. The dependent variable is the film i’s weekly box-office revenues of movie i

4 obtained in all the theatres in which it is exhibited in each country, in log terms, ln_Rev ict .

Films are one of the most highly differentiated products, as each movie is unique by nature, thus there are many characteristics that are unobservable and that may be important to explain its quality (see Einav, 2007). Therefore, we include film-specific constant terms

αi that capture these unobserved film characteristics and observed features that remain fixed over time such as the presence of stars on the movie, the genre, moral quantification, budget, etc. We also include country-specific effects αc to capture the observed and unobserved characteristics of each national market such as market size, attractiveness of movies as a leisure activity, etc. Additionally, we introduce the interactions between country and film specific effects to allow perceived quality to differ from one country or market to another.

Since the weekly box office revenues typically decay over the movies’ life, we include a set of dummy variables αt for each exhibition week to captures the average pattern over the movie run, common for all movies in the five markets. In addition, we introduce the interactions between movie and exhibition week dummy variables, since the temporal pattern of revenues might be affected by the characteristics of films. Similarly, we

4 Due to data limitations, the dependent variable in France is weekly attendance instead of weekly box-office revenues. In the estimated model revenues are in log terms and differences in t for each film in each country are taken but considering attendance in France instead implies having to assume that the price for each movie in this country does not change from one week to the next.

7 introduce the interactions between country and exhibition week dummy variables, to allow the typical time path of revenues to differ across countries (due to different structure and characteristics of national markets).

Additionally, as a proxy of the film’s availability we include the number of theatres in which the movie is exhibited each week in each country, ln_thrs , in log terms. To control for the seasonality in underlying demand we include a set of dummy variables for each week of the year αw.

Finally, to measure the impact of nominations and awards, we include a set of dummy variables that identify the movies that have been nominated or have won an Oscar in the different categories, from the week in which the event take place. It means that Dwin

(j) take value one in the week in which the movie won the Oscar and in the following weeks. The subscript j refers to the different categories of Oscars considered, namely: best picture, director, actor, actress, supporting actor, supporting actress, original screenplay and adapted screenplay. In order to test whether the effects of being nominated vanish once the

Oscars are awarded, continue being relevant or even become higher, we construct the following variables. Dnom_before (j) take value one in the week in which the movie was nominated and in the following weeks, before the Oscar ceremony, and it is set equal to zero after that. In the other side, Dnom_after (j) take value one after the Oscar ceremony for those movies that were nominated but did not received the Oscars.

The panel structure of our data allows us to control not only for the individual effects of each film, each national market, and each exhibition week over the movies’ life, but also for the interaction effect of all these dummy variables. To remove all the individual heterogeneity of movies and national markets, we apply first differences in t by movie within each country, before estimating the model. 8

∆_ = + + + + ∆_ℎ

+ ∆ _() + ∆ _()

+ ∆ ()

+ ∆ (2)

Note that, under this specification movie and country specific effects are removed, as well as their interactions terms. We estimate this model using ordinary least squares

(OLS) estimator. This estimation strategy is analogous to the diff in diff specification since we take first differences and include the dummy variables that identified the movies nominated and awarded an Oscar (the treatment).

[Table 1 about here]

We present in Table 1 a summary of the descriptive statistics up to the 20th exhibition week. Due to the size of the market, movies in the USA, on average, collect higher revenues and are exhibited in a larger number of theatres than in European countries.

Aside from the former, it is also noticeable that there is a lower prevalence of observations of weeks after a film has been nominated or awarded an Oscar in the USA than in the

European countries. Therefore, our sample contains a larger number of weeks before nominations for the USA market observations, where all the dummies regarding the Oscars are set equal to zero. This could be due to the fact that most of the movies racing for the

Oscars are released before the end of the year in the USA -in order to be eligible- and, usually, they are released later in Europe in order to take as much commercial advantage as possible of the “free” promotion of the nominations and awards. Evidence on this is shown in Figure 1, which displays the number of films released by week in the USA and the

European countries, comparing all films with those films that have been nominated to any major Oscar category. The first graph shows that movies are released following a similar pattern in the USA and the European countries. However, the second graph shows that the pattern is completely different regarding the films that obtained any major Oscar nominations. In USA these movies are more likely to be released at the end of the year. In

Europe, the release pattern for nominated movies is quite similar to the general one with two clearer modal periods at the beginning and the end of the year.

[Figure 1 about here]

Table 2 shows the run length in weeks by country. It can be observed that the commercial life of movies in the USA is larger than in Europe. In the USA, almost three- quarters of the movies remain more than 10 weeks on screen, and more than 40 per cent more than 15 weeks. The pattern is quite similar for European countries in the sample, except for France. In the French sample, almost two-thirds of the movies do not reach 10 weeks on screen and only 7 per cent of the movies manage to do so for more than 15 weeks. However, in the other three European countries, more than half of the films remain more than 10 weeks and more than 25 per cent more than 15 weeks. It is important to mention that domestic films are more relevant in the French market than in the other countries and, therefore, Hollywood films are less important. 5 With our dataset we cannot check whether French domestic movies have larger runs than international films in France

5 For the period 2002-2010, in France, national films account for 38 percent of total box-office revenues, while in Germany, Spain and the UK represent 18, 14 and 24 percent respectively. On the other hand, the market share of USA films was 49 percent in France and between 68 and 74 percent in the other three countries, for the same period (Centre national du cinéma et de l'image animée, 2017).

10 but it is clear that these have shorter runs than in other countries. In any case, by the twentieth week most of the films are no longer on screen. Given these patterns in the run length of movies, in the analysis we consider a time frameworks of the first 20 weeks.

[Table 2 about here]

Since we want to analyse the impact on box office revenue of the major Oscar categories, we will check whether they capture different quality values or if this is not the case, whether including all of them in a regression model leads to collinearity problems.

Table 3 presents the correlation matrix between the Oscar nominations and awards considered in our analysis. In general terms, as expected, correlation coefficients are higher among nomination categories and lower within winners. Also, most of the correlation coefficients are quite low with many close to zero; however there are three categories with a strong link: best picture, best director and best adapted screenplay, more as nominations than as winners. We will see below that this has an effect on the estimated coefficients of the empirical model.

[Table 3 about here]

4. Estimation and Results

Considering a time framework of 20 weeks 6, we estimate five models that differ in terms of the set of dummies referring to the major Oscar nominations and awards. We have estimated the potential impact on box office of the nominations and eventual awards to the best picture award in all the models. Model 2 also includes the best director category.

6 The results do not change significantly when we estimate the models including movies up to their 40th week on screen. Results are available upon request.

Model 3 focuses on the impact of best actor/actress. The screenplay awards are included in

Model 4 and, finally, the impact of all these categories are analysed together in Model 5.

Before analysing the impact of nominations and awards in the different categories, we discuss the estimated seasonality (i.e. the set of dummy variables for each week of the year). Figure 2 shows that the seasonal variations in the underlying demand have significant effects on movie performance, explaining almost 60% of the intra-annual variations, once other factors have been controlled for. In Table 4, we observe that the estimated elasticity of (weekly) box-office revenues with respect to the number of theatres are positive (around

0.65) and statistically significant in all models, as expected.

Now we focus our analysis on the variables that identify the nominations and awards. As the estimated models are defined, the coefficients for these dummies capture the percent impact on revenues linked to a nomination or award from the specific week when they become known onwards. In Model 1, consider the best picture category. The results imply that films that become nominated in the best film category will collect 22 percent more at the box office, from the week they are nominated to the Oscars ceremony day, and after that they will collect 36 per cent more (if they are no winner). It is worth noting that the impact of the nomination after the awards ceremony is significantly higher than before. 7

These results may be because the audience pay more attention to the nominated films after the awards are given. However, it seems that it does matter who wins, because films that

7 The value for the F(1, 2062) statistic is 16.10, above any critical value, so we strongly reject the null hypothesis that both effects are equal.

12 win the Oscar to best picture will increase their revenues by 79 percent, and this impact is significantly higher than the one for films that do not win. 8

These results may explain the recent decision of the Academy to raise the number of nominations in this particular category. As pointed out above, films nominated to best picture are usually nominated to other Oscar categories, especially to best director and best adapted screenplay. Therefore, when only the best picture nomination and award is considered, the estimated impact on revenues may also capture the effect of other nominations and awards. In any case, these estimations can be viewed as an upper limit.

In order to clean up the impact on revenues of the best picture nominations and awards, in Models 2-5, we include and remove from the estimations some of the major

Oscar categories. As expected, in all of them the estimated coefficients for the best picture nominations and awards are lower than in Model 1.

[Table 4 about here]

In Model 2, we include dummies for the best director category. Regarding this category, neither being nominated nor winning an Oscar show a statistically significant impact on revenues when the best picture category is considered too. Furthermore, the estimated coefficients of the dummies for the best film category are smaller than in Model

1. All these results may be due to the high correlation between both categories (see Table

3). Although the best director category does not seem to be as significant as the best picture category in terms of its impact on revenues, its high correlation with the best picture award may justify the reason why the studios are interested in hiring directors with greater

8 We obtain a value of 15.13, which far exceeds any critical value of an F (1, 2062). Therefore, we reject the null hypothesis that both effects are equal.

13 probabilities of being nominated as best director. Nominated directors have an indirect impact on revenues through the increased probability of also receiving a nomination/award for the best picture category; this impact may have an influence on their probability of being hired and their wages.

In Model 3, apart from the best film category, we include the best actor/actress and supporting actor/actress categories. The results show that having a nomination or winning an Oscar is important, in terms of revenues, in the best actor category but not in the best actress category. In addition, we observe that the impact of a nomination in the best actor category is important before the Oscars ceremony, but then it is vanished. Also, no significant effects were estimated regarding supporting actors or actresses. Actor awards are more relevant for the commercial success of a film than those linked to directors. But it is important to highlight that actors have only a direct effect on revenues since the correlation between this category and best picture is really low.

In Model 4 we include the original and the adapted screenplay categories. The original screenplay nominations have an average impact on weekly revenues of about 12 percent before the awards ceremony, but this impact disappears in the following weeks.

And winning an Oscar in this category has an impact of almost 27 percent. However, no significant effects were estimated regarding the adapted screenplay category. Note that the correlation among Oscar nominations for best picture and adapted screenplay is substantial

(around 0.6). Therefore, in our sample although many pictures were nominated or won the best picture Oscar, these were also nominated or won the best adapted screenplays; commercial glory seems to go hand in hand with the most important award of these categories. Following the same argument as with directors, studios may also have incentives to hire writers with a high probability of being nominated since either they could 14 increase revenues directly if they get an original script nomination/award or do so via an indirect effect if they obtain an adapted screenplay nomination/award, given the correlation between this category and the best picture category.

Finally, Model 5 includes all the above-mentioned categories simultaneously. In this case, most of the categories that were significant when they were included separately remain significant but in some cases with a lower estimated coefficient, although, these drops are not statistically significant. The exceptions are the best picture and the best original screenplay nominations that are no longer significant. Also, the best picture award suffers an important drop that is not statistically significant.

5. Robustness check

In the previous section, we consider the number of theatres in which the movie is exhibited as a proxy of the film’s availability. But it is very likely that exhibitors respond to nomination and wins announcements increasing or reducing the number of screens. Then, the evolution of number of theatres over time might be considered endogenous because their partially depends on the movie’s performance (Neelamegham and Chintagunta 1999).

We make a robustness check excluding theatres from the specification, given the possible endogeneity issues, even when we are including the interactions of exhibition week dummy variables with movie and country dummy variables. 9

9 This set of interactions attempt to control for factors that could affect the evolution of movie performance over time, across countries.

Table 5 shows that, in general, the commercial impact of the Oscar categories found in the previous specification continues being statistically significant, with some exceptions.

First, now the nominations to best actor before the Oscar ceremony are not statistically significant (Model 3 and 5), while the award to best actress are (Model 3). Second, in

Model 4, original screenplay awards are now no longer significant.

[Table 5 about here]

However, it is noteworthy that the magnitude of the coefficients is higher in all cases.

For example, in Model 1, results in Table 5 show that movies nominated in the best film category, before the Oscars ceremony day, will collect 39 percent more; while results in

Table 4 show an impact of 22 percent. These differences arise because specification of

Table 5 does not include theatres as an explanatory variable. In this case, the estimated coefficients of the related Oscar variables capture the impact on the box office revenues of nominations or wins including the indirect effect that these nominations and wins may have through changes in the number of theatres that exhibit the movie. It this seems, here, we measure both, the effects of viewers responding to announcements of nominations and awards and the effects of exhibitors changing the number of screens in response

(Neelamegham and Chintagunta 1999).

6. Additional results

We found that having a nomination or winning an Oscar in many categories have a significant impact on revenues, although the commercial glory tends to accompany the best picture Academy Award. Previous results highlight the importance of the best picture category and could help to explain the recent decision of the Academy to raise the number

16 of nominations in this particular category. Thus, more movies can benefit of the free promotion and spotlight associated with a nomination to best picture category.

Therefore, in this section we focus on the most important category of the Oscars.

First, we analyse whether the increase in the number of nominations has affected the commercial impact of the Oscar in that category. Additionally, we see whether the commercial impact of the Oscar for the best film differs between countries and also, if the

2009 decision had different effects in the domestic and foreign markets.

In June 2009, was announced an increase from five to ten films to be nominated in the best picture category. The change came into effect for the Oscars awarded at the 2010 ceremony, which rewards the best films of 2009. Two years after this change, the Academy revised the rule again to allow a changing number of nominees each year, for a minimum of five but a maximum of ten. But, since that change, there have never been less than eight films nominated.

We check whether the impact of nominations to best picture on box office revenues has changed since the Academy’s decision. To do so, we include in our specification the interaction of a dummy variable for the Oscars awarded after 2009 with the dummy variables for nominations to best picture before and after the ceremony.

[Table 6 about here]

Results in Table 6, Model 2 show that the Academy's decision to increase the number of nominations to best film has significantly reduced the positive effect that this nomination had on the weekly box office revenues 10 . In fact, in the period before the 2009 decision, a nomination in the best film category increased the revenues for the weeks after

10 Model 1 in Table 6 repeats the results of Model 1 in Table 4 to facilitate reader comparison.

17 the ceremony by almost 45 percent. Meanwhile, after the 2009 decision, this impact is only

24 percent. On the other hand, we note that the impact of a nomination in the weeks leading up to the ceremony has not changed, it is still 21 percent.

In order to check whether the impact of nominations and awards differs by country we estimate Model 3 and 4, where we include in the specification the interaction of dummy variable for each country with the rest of dummy variables for nominations and awards. In

Model 3, when we focus on the impact of nominations on weekly box office before the

Oscars ceremony, we find that this impact is larger in Spain and statistically different from the rest of the countries. Meanwhile a nomination in the UK has no impact on the box office revenues of the weeks leading up to the ceremony. Regarding the impact of a nomination in the weeks after the ceremony, again, for Spain this impact is larger and significantly different from the other European countries. In addition, this impact is also statistically different between France and Germany. Regarding the awards, once again, the impact is larger in Spain; but it is also large in Germany and significantly different from the

UK.

Comparing the impact of a nomination in the weeks previous and after the ceremony, in all countries the impact is significantly higher after the awards ceremony, except in France, where the difference is not statistically significant. Additionally, we find that the impact of an award is higher than the impact of a nomination after the ceremony, as expected. Only in the case of USA there is no statistically significant difference. Given that in USA a nomination after the ceremony has the same impact on revenues than an award, there is a clear incentive to increase the number of nominations in the best film category.

In Model 4 we want to see whether the decision to increase the number of nominations had a different effect in the countries under analysis. Surprisingly, in Spain, 18 the impact of a nomination for the best film in the weeks leading up to the Oscar ceremony is even higher after 2009. It seems that, in Spain, more nominations attract more viewers for this type of films.

On the other hand, in the United States we find just the opposite results. After 2009, the increase in the number of nominations seems to dilute the impact that nominations have on the weekly revenues, not only before the ceremony, but also after the ceremony for those films that are not winners. These results could explain that some members of the

Academy leadership wanted to revisit the idea of returning to five nominations in 2015

(Galloway, 2015). The arguments were that having too many best picture nominees has watered down the prestige of a nomination. However, no decisions have yet been taken on this respect.

7. Conclusions

In this paper we have measured the impact on weekly box office revenues of the major Academy Award nominations and awards (best picture, best director, best actor/actress, best supporting actor/actress and best original and adapted screenplay). We distinguished between the effect of a nomination before and after the Oscar ceremony, and the effect of winning the Oscar. Then, we focused on the most important category, the best film award, to check if the impacts have changed with the Academy’s decision to increase the number of nominations. In addition, we analysed whether the commercial impact of an

Oscar or a nomination in this category differs between the domestic market and foreign markets, taking into account the largest European markets.

Using weekly data on revenues from several markets and taking advantage of the fact that movies are usually released on different dates in the different countries, we were better able to measure the value of nominations and awards. This framework enables us to compare revenues before and after the announcement of nominations and awards and to assess the impact at particular points in time by matching running weeks in different countries, some before and others after the nominations. Furthermore, the three dimensions of this panel structure allows us to control not only for the unobserved heterogeneity of each film and each national market but also for the temporal decay pattern of the weekly box office revenues by film and by country. This including dummy variables for each exhibition week, and also the interactions between film and country specific effects.

Controlling for the unobserved heterogeneity inherent to movies is essential to explain quality, which is important because movies that receive a nomination or award are high quality movies. We therefore need to separate the effect of the award and the effect of the quality of the movie on box-office revenues. For this reason we also include the interactions between film and market specific effects to allow the perceived quality to differ by country.

We found that receiving a nomination for best picture will increase the weekly box office of a movie by about 22 per cent from the week in which nominations are announced to the Oscars ceremony day. Following that day, a nomination on this category will increase the weekly revenues by about 36 per cent and an Oscar about 79 per cent. It seems to be important who wins, because its impact is significantly higher that the impact of nominations. It is worth noting that the commercial impact of nominations after the ceremony day is significantly higher than before. This could be because many viewers do not pay attention and ignore which movies are nominated until the Oscars ceremony. In this 20 sense, the Oscars ceremony seems to be the key for the commercial purpose of these awards.

Regarding the performance categories, our results show that having a nomination or winning an Oscar in the best actor category has a significant impact on revenues. In fact, actor nominations are relevant for the commercial success of a film before the Oscars ceremony, not after that. However, no relevant effects were found for best actress and supporting actors/actress. This result is in line with the exorbitant salaries payed to male stars.

As regards the original screenplay category, nominations have an average impact on weekly revenues of about 12% before the ceremony, but this impact vanishes in the following weeks. Awards in this category have an impact of almost 27 percent.. However, the adapted screenplay category is less important and has not a significant impact on revenues. It is important to bear in mind that there is a high correlation between both categories (best director and adapted screenplay) and the best picture category. Therefore, although many films that were nominated or won the best picture Oscar were also nominated for or won the best director or adapted screenplays awards, it seems that the relevant signal of quality is the best movie category.

Consequently, film producers may be interested in hiring writers or directors with a high probability of being nominated, not for the impact on revenues that an Oscar or nomination in these categories would have but rather because it would raise the probability of obtaining an Oscar or a nomination for Best Picture. We have an example of the advertisement and promotion effects of these Oscars in two recent films: "12 Years a

Slave" and "Gravity". In 2013, the former was the winner of the Oscar for best picture and best-adapted screenplay, while the latter won the Oscar for best director and was nominated 21 to the best picture. Both movies started the year with a similar temporal pattern in revenues.

However, when winners were announced, "12 Years a Slave" had an increase in its box office revenues in the USA of 95%. “Gravity”, on the other hand, increased its box-office by only 14%. This illustrates how commercial success is especially associated with the best movie Academy Award. From this result, it is advisable to take advantage of the effect that this Oscar has on signalling a film's quality and, hence, on its commercial success. One possible measure, in line with the recent decision of the Academy to raise the number of nominations, could be to increase the number of winners by creating a second prize as a way of sharing the glory of award-winning.

In order to check the robustness of our findings, we have estimated alternative models where the number of theatres in which a movie is exhibited is not included as explanatory variable, given the possible endogeneity issues. The results are robust in sign and significance, but the commercial impacts of the Oscar are higher in all cases. The reason is that the estimated impacts capture the effects of viewers responding to the announcement of nominations and wins together with the effects of exhibitors changing the number of theatres accordingly.

The results highlight the importance of the best picture category. Our findings are in line with the decision taken by the academy in 2009 to increase the number of nominations in this category, thus more movies can take advantage of the free advertising and spotlight associated with a nomination to best picture. It is worth mentioning that this decision was taken by an industry dominated by the major studios, whose movies are more likely to receive these nominations. 11

11 For example, in 2013 and 2014 major studios received 63% of all nominations and 69% of all awards.

However, our findings show that the Academy's decision to increase the number of nominations in the best film category has not achieved the intended effects. In fact, it has reduced by almost half the positive effect a nomination had in the weeks after the ceremony. On the other hand, the effect of a nomination in the weeks leading up to the ceremony remains the same.

Apart from that, we have found certain differences in the commercial impact of nominations and awards by country, possibly due to the particular characteristics of these national markets. For instance, the impact of nominations on weekly box office before the

Oscars ceremony is larger in Spain than in U.S, Germany and France, meanwhile in the UK there is no impact.

In all countries, the impact of a nomination after the ceremony is higher than before that, except in France, where there is no difference. This is an important market-specific characteristic that distributors should take into account when designing the release strategy of a movie because the relative importance of the opening weekend varies from one country to another. And the impact of an award is higher than the impact of a nomination after the ceremony, with the exception of USA where the difference is no significant.

The last results in the domestic market create clear incentives in the industry to expand the number of nominations in the most important category. Paradoxically, we found that it is just in the domestic market where the 2009 Academy's decision has significantly reduced the impact of nominations on weekly revenues, both before and after the ceremony. This may have been the reason why in 2015 some members of the Academy were campaigning for a return to five best film nominations. The idea of the Academy's decision was to bring more popular films into play, but the Academy has nominated more of their kind of film, art-house films (Galloway, 2015 and Agar, 2016). 23

In conclusion, the estimated effects on revenues of different Oscar categories could be relevant for producers when taking decisions about budgets (allocation of resources) for movies that will run for these awards. The information about the differences in the commercial impact of nominations and awards by country could be useful for distributors when deciding the release schedules of these films in domestic and foreign markets.

Additionally, the differences between the effect of a nomination before and after the Oscars award may suggest an eventual redesign of the awards, as carried out by the Academy in recent years, varying the number of nominations or the timing of the announcements of nominations and awards. These changes should be oriented towards generating higher value for the industry.

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FIGURES AND TABLES

Figure 1 . Kernel density of movies releases by week and region

All movies Movies nominated for any major Oscar .05 .05 .04 .04 .03 .03 Density Density .02 .02 .01 .01 0 0

0 10 20 30 40 50 0 10 20 30 40 50 week week

USA USA France, Germany, Spain and UK EU

kernel = epanechnikov, bandwidth = 3.0907 kernel = epanechnikov, bandwidth = 3.9671

Figure 2 . Estimated seasonality in the underlying demand

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

value of the estimated theof coefficient value estimated -0.4

-0.5 2 4 6 8 10121416182022242628303234363840424446485052 week of the year

Notes: Plot shows estimated coefficients for the set of dummy variables for each week of the year for model 5 of table 4. All the estimated coefficients are statistically significant at 1% level.

Table 1. Descriptive Statistics up to the exhibition week #20

France Germany Spain United Kingdom USA

(n=9,658) (n=16,591) (n=17,193) (n=9,495) (n=17,581) Std. Std. Std. Std. Std. Variable Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max Dev. Dev. Dev. Dev. Dev. revenue (in thousands) 106.7 210.6 0.1 4,082 411 1,058 0 21,900 268 593 0 12,000 706 1,683 0 23,200 3,471 7,389 1 135,000 theatre 244 211 1 1,078 219 215 1 1,341 115 125 1 827 145 154 1 586 1,065 1,138 1 4,404 nom_picture_before 0.0152 0.1224 0 1 0.0115 0.1064 0 1 0.0158 0.1248 0 1 0.0147 0.1205 0 1 0.0184 0.1343 0 1 nom_picture_after 0.0272 0.1628 0 1 0.0317 0.1752 0 1 0.0306 0.1722 0 1 0.0220 0.1467 0 1 0.0117 0.1076 0 1 win_picture 0.0081 0.0895 0 1 0.0099 0.0989 0 1 0.0080 0.0892 0 1 0.0059 0.0766 0 1 0.0028 0.0527 0 1 nom_director_before 0.0124 0.1108 0 1 0.0085 0.0918 0 1 0.0116 0.1070 0 1 0.0146 0.1201 0 1 0.0132 0.1141 0 1 nom_director_after 0.0210 0.1435 0 1 0.0236 0.1517 0 1 0.0222 0.1472 0 1 0.0213 0.1443 0 1 0.0073 0.0853 0 1 win_director 0.0067 0.0818 0 1 0.0096 0.0974 0 1 0.0070 0.0836 0 1 0.0071 0.0837 0 1 0.0034 0.0583 0 1 nom_actor_before 0.0097 0.0982 0 1 0.0070 0.0833 0 1 0.0109 0.1040 0 1 0.0143 0.1188 0 1 0.0129 0.1129 0 1 nom_actor_after 0.0150 0.1216 0 1 0.0237 0.1521 0 1 0.0211 0.1438 0 1 0.0190 0.1364 0 1 0.0081 0.0898 0 1 win_actor 0.0070 0.0836 0 1 0.0083 0.0905 0 1 0.0060 0.0775 0 1 0.0076 0.0868 0 1 0.0024 0.0488 0 1 nom_actress_before 0.0057 0.0753 0 1 0.0052 0.0718 0 1 0.0077 0.0873 0 1 0.0067 0.0818 0 1 0.0100 0.0996 0 1 nom_actress_after 0.0100 0.0997 0 1 0.0122 0.1097 0 1 0.0126 0.1116 0 1 0.0106 0.1026 0 1 0.0044 0.0665 0 1 win_actress 0.0084 0.0912 0 1 0.0089 0.0937 0 1 0.0075 0.0863 0 1 0.0075 0.0862 0 1 0.0041 0.0639 0 1 nom_sup.actor_before 0.0120 0.1089 0 1 0.0073 0.0851 0 1 0.0097 0.0978 0 1 0.0097 0.0980 0 1 0.0109 0.1037 0 1 nom_sup.actor_after 0.0148 0.1208 0 1 0.0174 0.1306 0 1 0.0154 0.1232 0 1 0.0156 0.1239 0 1 0.0046 0.0677 0 1 win_sup.actor 0.0053 0.0725 0 1 0.0061 0.0782 0 1 0.0069 0.0829 0 1 0.0045 0.0671 0 1 0.0010 0.0311 0 1 nom_sup.actress_before 0.0068 0.0824 0 1 0.0065 0.0804 0 1 0.0105 0.1021 0 1 0.0104 0.1016 0 1 0.0099 0.0990 0 1 nom_sup.actress_after 0.0121 0.1094 0 1 0.0201 0.1405 0 1 0.0169 0.1290 0 1 0.0156 0.1239 0 1 0.0066 0.0810 0 1 win_sup.actress 0.0052 0.0718 0 1 0.0063 0.0793 0 1 0.0044 0.0663 0 1 0.0062 0.0786 0 1 0.0015 0.0392 0 1 nom_orig.screenplay_before 0.0083 0.0906 0 1 0.0064 0.0797 0 1 0.0080 0.0892 0 1 0.0063 0.0792 0 1 0.0076 0.0866 0 1 nom_orig.screenplay_after 0.0074 0.0854 0 1 0.0132 0.1141 0 1 0.0133 0.1144 0 1 0.0094 0.0964 0 1 0.0035 0.0588 0 1 win_orig.screenplay 0.0069 0.0830 0 1 0.0075 0.0861 0 1 0.0059 0.0764 0 1 0.0050 0.0702 0 1 0.0013 0.0354 0 1 nom_adap.screenplay_before 0.0100 0.0997 0 1 0.0074 0.0858 0 1 0.0095 0.0969 0 1 0.0107 0.1031 0 1 0.0123 0.1104 0 1 nom_adap.screenplay_after 0.0157 0.1245 0 1 0.0193 0.1375 0 1 0.0181 0.1335 0 1 0.0150 0.1214 0 1 0.0078 0.0883 0 1 win_adap.screenplay 0.0060 0.0773 0 1 0.0081 0.0895 0 1 0.0067 0.0819 0 1 0.0075 0.0862 0 1 0.0024 0.0488 0 1 * In France the dependent variable is weekly attendance instead of weekly box-office revenues.

Table 2. Maximum number of weeks on screen Max. number of FR GR SP UK USA weeks on screen 5 140 53 28 50 32 6 184 73 71 95 50 7 151 109 122 92 82 8 158 151 154 90 65 9 124 131 134 73 77 10 124 158 154 75 106 11 76 94 137 56 114 12 67 128 126 48 110 13 33 86 99 37 91 14 36 79 86 47 115 15 33 54 88 46 99 16 11 55 59 42 107 17 11 53 50 26 84 18 9 41 44 26 71 14 26 34 23 63

20 7 36 29 19 49 21 5 26 31 11 52 22 4 22 32 16 32 23 0 11 17 16 32 24 5 16 19 15 29 25 1 13 22 6 19 26-30 8 45 51 20 67 31-35 4 25 23 10 25 36-40 2 11 8 5 12 +40 5 61 22 13 39 Total 1,212 1,557 1,640 957 1,622

Table 3. Correlation matrix between major Oscar nominations and awards (evaluated once per movie)

Best Leading Leading Supporting Supporting Original Categories Director picture actor actress actor actress screenplay awards Best picture 1 Director 0.6339 1 Leading actor 0.0847 0.1762 1 Leading actress 0.0805 0.0805 -0.0071 1 Supporting actor 0.1682 0.1682 0.1682 0.0765 1 Supporting actress 0.1611 -0.0074 -0.0074 -0.0078 0.0729 1 Original screenplay 0.1961 0.0950 0.0950 -0.0064 0.0904 -0.0067 1 Adapted screenplay 0.4981 0.4981 0.0768 -0.0078 0.0729 0.1470 -0.0067 Nominations Best picture 1 Director 0.7750 1 Leading actor 0.4965 0.4867 1 Leading actress 0.2688 0.1893 0.0638 1 Supporting actor 0.4083 0.3493 0.2941 0.1746 1 Supporting actress 0.3901 0.4147 0.3994 0.2117 0.3351 1 Original screenplay 0.3999 0.3682 0.2338 0.1656 0.2634 0.2249 1 Adapted screenplay 0.6222 0.5028 0.4185 0.2103 0.3173 0.3492 -0.0357

Table 4. OLS Regressions

∆ln_Rev Model 1 Model 2 Model 3 Model 4 Model 5 Variable Coefficient Rob. Std. Err. Coefficient Rob. Std. Err. Coefficient Rob. Std. Err. Coefficient Rob. Std. Err. Coefficient Rob. Std. Err. ∆ln_theatre 0.6529*** [0.009] 0.6528*** [0.009] 0.6527*** [0.009] 0.6528*** [0.009] 0.6526*** [0.009] ∆nom_picture_before 0.2157*** [0.036] 0.1619 [0.101] 0.1392*** [0.049] 0.1741** [0.071] 0.1216 [0.119] ∆nom_picture_after 0.3574*** [0.055] 0.2522* [0.135] 0.3029*** [0.072] 0.2488** [0.108] 0.1526 [0.158] ∆win_picture 0.7895*** [0.113] 0.6262*** [0.191] 0.6954*** [0.123] 0.5998*** [0.154] 0.4440** [0.185] ∆nom_director_before 0.0688 [0.103] 0.0101 [0.099] ∆nom_director_after 0.1336 [0.143] 0.0593 [0.136] ∆win_director 0.2004 [0.187] 0.0503 [0.159] ∆nom_actor_before 0.1143** [0.049] 0.1040** [0.048] ∆nom_actor_after 0.0587 [0.072] 0.0374 [0.072] ∆win_actor 0.2045** [0.100] 0.1912** [0.096] ∆nom_actress_before 0.0557 [0.046] 0.0494 [0.045] ∆nom_actress_after -0.0359 [0.066] -0.0349 [0.065] ∆win_actress 0.1075 [0.078] 0.1275* [0.072] ∆nom_sup.actor_before -0.0246 [0.055] -0.0244 [0.054] ∆nom_sup.actor_after 0.0068 [0.081] -0.0118 [0.083] ∆win_sup.actor 0.0786 [0.097] 0.0608 [0.101] ∆nom_sup.actress_before 0.0499 [0.036] 0.0363 [0.039] ∆nom_sup.actress_after -0.0220 [0.053] -0.0631 [0.056] ∆win_sup.actress 0.0907 [0.089] 0.1041 [0.091] ∆nom_orig.screenplay_before 0.1220* [0.063] 0.0903 [0.060] ∆nom_orig.screenplay_after 0.1442 [0.116] 0.1574 [0.121] ∆win_orig.screenplay 0.2659** [0.120] 0.2775** [0.124] ∆nom_adap.screenplay_before -0.0048 [0.075] -0.0220 [0.071] ∆nom_adap.screenplay_after 0.0492 [0.105] 0.0877 [0.108] ∆win_adap.screenplay 0.1912 [0.158] 0.2374 [0.147]

α0 -0.0884*** [0.018] -0.0883*** [0.018] -0.0891*** [0.018] -0.0884*** [0.018] -0.0890*** [0.018] Number of obs. 70,518 70,518 70,518 70,518 70,518 Number of movies 2,063 2,063 2,063 2,063 2,063 BIC 41,951 41,982 42,060 42,005 42,146 AIC 40,741 40,745 40,741 40,740 40,744 Notes: OLS estimates for specification (2). Estimated models include movie, exhibition week and country dummies, as well as interactions of all these dummy variables. Robust standard errors in parentheses clustered at the movie level. Legend: ∗p <0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Table 5. OLS Regressions

∆ln_Rev Model 1 Model 2 Model 3 Model 4 Model 5 RobustStd. RobustStd. RobustStd. RobustStd. RobustStd. Variable Coefficient Coefficient Coefficient Coefficient Coefficient Err. Err. Err. Err. Err.

∆nom_picture_before 0.3940*** [0.073] 0.3555** [0.165] 0.2956*** [0.084] 0.3438*** [0.125] 0.3015 [0.203] ∆nom_picture_after 0.5115*** [0.081] 0.3350* [0.187] 0.4434*** [0.110] 0.4825*** [0.169] 0.3115 [0.240] ∆win_picture 1.1643*** [0.184] 0.8924*** [0.310] 1.0761*** [0.196] 1.0233*** [0.236] 0.8225*** [0.286] ∆nom_director_before 0.0486 [0.176] -0.0172 [0.164] ∆nom_director_after 0.2293 [0.203] 0.1204 [0.193] ∆win_director 0.3201 [0.301] 0.0576 [0.252] ∆nom_actor_before 0.0991 [0.091] 0.0916 [0.089] ∆nom_actor_after 0.0141 [0.110] 0.0146 [0.110] ∆win_actor 0.2944** [0.147] 0.2817** [0.132] ∆nom_actress_before 0.0952 [0.083] 0.0840 [0.080] ∆nom_actress_after -0.0993 [0.102] -0.0661 [0.099] ∆win_actress 0.2664** [0.127] 0.2925** [0.129] ∆nom_sup.actor_before 0.0182 [0.093] 0.0187 [0.097] ∆nom_sup.actor_after 0.0479 [0.122] 0.0170 [0.129] ∆win_sup.actor 0.0270 [0.162] 0.0726 [0.172] ∆nom_sup.actress_before 0.0524 [0.073] 0.0321 [0.077] ∆nom_sup.actress_after -0.0420 [0.089] -0.0801 [0.092] ∆win_sup.actress 0.0460 [0.131] 0.0529 [0.127] ∆nom_orig.screenplay_before 0.1812* [0.110] 0.1415 [0.103] ∆nom_orig.screenplay_after 0.0929 [0.179] 0.1208 [0.183] ∆win_orig.screenplay 0.3024 [0.192] 0.3032* [0.180] ∆nom_adap.screenplay_before -0.0308 [0.126] -0.0566 [0.131] ∆nom_adap.screenplay_after -0.1215 [0.156] -0.0775 [0.166] ∆win_adap.screenplay 0.1311 [0.229] 0.2043 [0.221]

α0 -0.2126*** [0.021] -0.2124*** [0.021] -0.2134*** [0.021] -0.2128*** [0.021] -0.2134*** [0.021] Number of obs. 70,531 70,531 70,531 70,531 70,531 Number of movies 2,063 2,063 2,063 2,063 2,063 BIC 67,893,304 67,918,938 67,985,315 67,936,253 68,063,711 AIC 66,692,845 66,690,987 66,674,891 66,680,811 66,670,812 Notes: OLS estimates for specification (2). Estimated models include movie, exhibition week and country dummies, as well as interactions of all these dummy variables. Robust standard errors in parentheses clustered at the movie level. Legend: ∗p <0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

Table 6. OLS Regressions

∆ln_Rev Model 1 Model 2 Model 3 Model 4 Variable Coefficient Coefficient Coefficient Coefficient ∆ln_theatre 0.6529*** [0.009] 0.6529*** [0.009] 0.6527*** [0.009] 0.6527*** [0.009] ∆nom_picture_before 0.2157*** [0.036] 0.2099*** [0.044] ∆nom_picture_after 0.3574*** [0.055] 0.4480*** [0.062] ∆win_picture 0.7895*** [0.113] 0.7893*** [0.111] ∆nom_picture_before_past2009 0.0200 [0.067] ∆nom_picture_after_past2009 -0.2053** [0.101] ∆nom_picture_before_ES 0.4001*** [0.059] 0.2889*** [0.071] ∆nom_picture_before_FR 0.1568** [0.070] 0.1626** [0.065] ∆nom_picture_before_GR 0.1849*** [0.054] 0.1199* [0.063] ∆nom_picture_before_UK 0.0730 [0.056] 0.0748 [0.056] ∆nom_picture_before_USA 0.2135*** [0.062] 0.3377*** [0.081] ∆nom_picture_after_ES 0.5618*** [0.079] 0.5937*** [0.104] ∆nom_picture_after_FR 0.1851** [0.081] 0.2268*** [0.073] ∆nom_picture_after_GR 0.3609*** [0.076] 0.3471*** [0.095] ∆nom_picture_after_UK 0.2684*** [0.084] 0.2825*** [0.084] ∆nom_picture_after_USA 0.3716*** [0.115] 0.6943*** [0.148] ∆win_picture_ES 1.0910*** [0.129] 1.0678*** [0.126] ∆win_picture_FR 0.6860*** [0.145] 0.6831*** [0.136] ∆win_picture_GR 0.8367*** [0.142] 0.8702*** [0.152] ∆win_picture_UK 0.5422*** [0.086] 0.5490*** [0.086] ∆win_picture_USA 0.6810*** [0.190] 0.7335*** [0.195] ∆nom_picture_before_ES_past2009 0.2459** [0.102] ∆nom_picture_before_FR_past2009 -0.0305 [0.181] ∆nom_picture_before_GR_past2009 0.1842 [0.118] ∆nom_picture_before_UK_past2009 0.0000 [.] ∆nom_picture_before_USA_past2009 -0.2129* [0.110] ∆nom_picture_after_ES_past2009 -0.0783 [0.140] ∆nom_picture_after_FR_past2009 -0.1032 [0.202] ∆nom_picture_after_GR_past2009 0.0816 [0.152] ∆nom_picture_after_UK_past2009 0.0000 [.] ∆nom_picture_after_USA_past2009 -0.5939*** [0.209]

α0 -0.0884*** [0.018] -0.0883*** [0.018] -0.0878*** [0.018] -0.0876*** [0.018] Number of obs. 70,518 70,518 70,518 70,518 Number of movies 2,063 2,063 2,063 2,063 BIC 41.951.051 41.958.988 42.065.187 42.122.971 AIC 40.741.452 40.731.062 40.745.625 40.730.101 Notes: OLS estimates for specification (2). Estimated models include movie, exhibition week and country dummies, as well as interactions of all these dummy variables. Robust standard errors in parentheses clustered at the movie level. Legend: ∗p <0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.